The rising interest of primary cilia in central nervous system malignancies: A bibliometric and altmetric analysis revealing the growth of a field
Bibliographic record
Abstract
Primary cilia have been implicated in a myriad of central nervous system malignancies. Despite the bleak prognoses of these diseases, remarkable advancements are being made to understanding the molecular mechanisms of primary cilia as a modulator in these diseases. To better comprehend the present academic endeavors in this neuro-oncology domain, we examined and assessed bibliometric and altmetric features of the primary cilia articles as they pertain to primary CNS malignancies. All primary journal articles regarding primary cilia and central nervous system (CNS) tumors were acquired by searching the Clarivate Analytics’ Web of Science Core Collection (WoSCC) and Pubmed searches. Bibliometric and altmetric analyses, data cleanup, and data visualization were conducted using InCites, Altmetric Explorer and VOSviewer. Funding information was obtained from the NIH RePORTER database. The WoSCC search yielded 133 unique articles that met the study criteria, and 45 additional articles were acquired from Pubmed searches and integrated into WoS resulting a total of 178 articles analyzed. The 10 most-cited articles were also evaluated. For this study, all articles were generated within the United States and published by a total 42 journals. As the role of primary cilia in CNS cancer continues to be explored, the amount of funding allocated, and publications released steadily increases annually. The findings provided by basic science articles drive our understanding of primary cilia as they relate to neuro-oncology. Bibliometric and altimetric analyses are significant contributors in the comprehension of current literature and for the identification of knowledge gaps. Ultimately these analyses encourage future research pursuits in the creation of ciliotherapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.157 | 0.178 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".